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 "cells": [
  {
   "source": [
    "**PCA实战task：**  \n",
    "1、基于iris_data.csv数据，建立KNN模型实现数据分类（n_neighbors=3）  \n",
    "2、对数据进行标准化处理，选取一个维度可视化处理后的效果  \n",
    "3、进行与原数据等维度PCA，查看各主成分的方差比例  \n",
    "4、保留合适的主成分，可视化降维后的数据  \n",
    "5、基于降维后数据建立KNN模型，与原数据表现进行对比  "
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   sepal length  sepal width  petal length  petal width       target  label\n0           5.1          3.5           1.4          0.2  Iris-setosa      0\n1           4.9          3.0           1.4          0.2  Iris-setosa      0\n2           4.7          3.2           1.3          0.2  Iris-setosa      0\n3           4.6          3.1           1.5          0.2  Iris-setosa      0\n4           5.0          3.6           1.4          0.2  Iris-setosa      0",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>sepal length</th>\n      <th>sepal width</th>\n      <th>petal length</th>\n      <th>petal width</th>\n      <th>target</th>\n      <th>label</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>5.1</td>\n      <td>3.5</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>Iris-setosa</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4.9</td>\n      <td>3.0</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>Iris-setosa</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4.7</td>\n      <td>3.2</td>\n      <td>1.3</td>\n      <td>0.2</td>\n      <td>Iris-setosa</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.6</td>\n      <td>3.1</td>\n      <td>1.5</td>\n      <td>0.2</td>\n      <td>Iris-setosa</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5.0</td>\n      <td>3.6</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>Iris-setosa</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 42
    }
   ],
   "source": [
    "# 载入数据\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "data = pd.read_csv('iris_data.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   sepal length  sepal width  petal length  petal width\n0           5.1          3.5           1.4          0.2\n1           4.9          3.0           1.4          0.2\n2           4.7          3.2           1.3          0.2\n3           4.6          3.1           1.5          0.2\n4           5.0          3.6           1.4          0.2",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>sepal length</th>\n      <th>sepal width</th>\n      <th>petal length</th>\n      <th>petal width</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>5.1</td>\n      <td>3.5</td>\n      <td>1.4</td>\n      <td>0.2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4.9</td>\n      <td>3.0</td>\n      <td>1.4</td>\n      <td>0.2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4.7</td>\n      <td>3.2</td>\n      <td>1.3</td>\n      <td>0.2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.6</td>\n      <td>3.1</td>\n      <td>1.5</td>\n      <td>0.2</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5.0</td>\n      <td>3.6</td>\n      <td>1.4</td>\n      <td>0.2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 43
    }
   ],
   "source": [
    "# 定义X，y\n",
    "X = data.drop(['target', 'label'], axis=1)\n",
    "y = data.loc[:, 'label']\n",
    "X.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "0.96\n"
    }
   ],
   "source": [
    "# 建立KNN模型\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "KNN = KNeighborsClassifier(n_neighbors=3)\n",
    "KNN.fit(X, y)\n",
    "y_predict = KNN.predict(X)\n",
    "accuracy = accuracy_score(y, y_predict)\n",
    "print(accuracy)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   }
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "[[-9.00681170e-01  1.03205722e+00 -1.34127240e+00 -1.31297673e+00]\n [-1.14301691e+00 -1.24957601e-01 -1.34127240e+00 -1.31297673e+00]\n [-1.38535265e+00  3.37848329e-01 -1.39813811e+00 -1.31297673e+00]\n [-1.50652052e+00  1.06445364e-01 -1.28440670e+00 -1.31297673e+00]\n [-1.02184904e+00  1.26346019e+00 -1.34127240e+00 -1.31297673e+00]\n [-5.37177559e-01  1.95766909e+00 -1.17067529e+00 -1.05003079e+00]\n [-1.50652052e+00  8.00654259e-01 -1.34127240e+00 -1.18150376e+00]\n [-1.02184904e+00  8.00654259e-01 -1.28440670e+00 -1.31297673e+00]\n [-1.74885626e+00 -3.56360566e-01 -1.34127240e+00 -1.31297673e+00]\n [-1.14301691e+00  1.06445364e-01 -1.28440670e+00 -1.44444970e+00]\n [-5.37177559e-01  1.49486315e+00 -1.28440670e+00 -1.31297673e+00]\n [-1.26418478e+00  8.00654259e-01 -1.22754100e+00 -1.31297673e+00]\n [-1.26418478e+00 -1.24957601e-01 -1.34127240e+00 -1.44444970e+00]\n [-1.87002413e+00 -1.24957601e-01 -1.51186952e+00 -1.44444970e+00]\n [-5.25060772e-02  2.18907205e+00 -1.45500381e+00 -1.31297673e+00]\n [-1.73673948e-01  3.11468391e+00 -1.28440670e+00 -1.05003079e+00]\n [-5.37177559e-01  1.95766909e+00 -1.39813811e+00 -1.05003079e+00]\n [-9.00681170e-01  1.03205722e+00 -1.34127240e+00 -1.18150376e+00]\n [-1.73673948e-01  1.72626612e+00 -1.17067529e+00 -1.18150376e+00]\n [-9.00681170e-01  1.72626612e+00 -1.28440670e+00 -1.18150376e+00]\n [-5.37177559e-01  8.00654259e-01 -1.17067529e+00 -1.31297673e+00]\n [-9.00681170e-01  1.49486315e+00 -1.28440670e+00 -1.05003079e+00]\n [-1.50652052e+00  1.26346019e+00 -1.56873522e+00 -1.31297673e+00]\n [-9.00681170e-01  5.69251294e-01 -1.17067529e+00 -9.18557817e-01]\n [-1.26418478e+00  8.00654259e-01 -1.05694388e+00 -1.31297673e+00]\n [-1.02184904e+00 -1.24957601e-01 -1.22754100e+00 -1.31297673e+00]\n [-1.02184904e+00  8.00654259e-01 -1.22754100e+00 -1.05003079e+00]\n [-7.79513300e-01  1.03205722e+00 -1.28440670e+00 -1.31297673e+00]\n [-7.79513300e-01  8.00654259e-01 -1.34127240e+00 -1.31297673e+00]\n [-1.38535265e+00  3.37848329e-01 -1.22754100e+00 -1.31297673e+00]\n [-1.26418478e+00  1.06445364e-01 -1.22754100e+00 -1.31297673e+00]\n [-5.37177559e-01  8.00654259e-01 -1.28440670e+00 -1.05003079e+00]\n [-7.79513300e-01  2.42047502e+00 -1.28440670e+00 -1.44444970e+00]\n [-4.16009689e-01  2.65187798e+00 -1.34127240e+00 -1.31297673e+00]\n [-1.14301691e+00  1.06445364e-01 -1.28440670e+00 -1.44444970e+00]\n [-1.02184904e+00  3.37848329e-01 -1.45500381e+00 -1.31297673e+00]\n [-4.16009689e-01  1.03205722e+00 -1.39813811e+00 -1.31297673e+00]\n [-1.14301691e+00  1.06445364e-01 -1.28440670e+00 -1.44444970e+00]\n [-1.74885626e+00 -1.24957601e-01 -1.39813811e+00 -1.31297673e+00]\n [-9.00681170e-01  8.00654259e-01 -1.28440670e+00 -1.31297673e+00]\n [-1.02184904e+00  1.03205722e+00 -1.39813811e+00 -1.18150376e+00]\n [-1.62768839e+00 -1.74477836e+00 -1.39813811e+00 -1.18150376e+00]\n [-1.74885626e+00  3.37848329e-01 -1.39813811e+00 -1.31297673e+00]\n [-1.02184904e+00  1.03205722e+00 -1.22754100e+00 -7.87084847e-01]\n [-9.00681170e-01  1.72626612e+00 -1.05694388e+00 -1.05003079e+00]\n [-1.26418478e+00 -1.24957601e-01 -1.34127240e+00 -1.18150376e+00]\n [-9.00681170e-01  1.72626612e+00 -1.22754100e+00 -1.31297673e+00]\n [-1.50652052e+00  3.37848329e-01 -1.34127240e+00 -1.31297673e+00]\n [-6.58345429e-01  1.49486315e+00 -1.28440670e+00 -1.31297673e+00]\n [-1.02184904e+00  5.69251294e-01 -1.34127240e+00 -1.31297673e+00]\n [ 1.40150837e+00  3.37848329e-01  5.35295827e-01  2.64698913e-01]\n [ 6.74501145e-01  3.37848329e-01  4.21564419e-01  3.96171883e-01]\n [ 1.28034050e+00  1.06445364e-01  6.49027235e-01  3.96171883e-01]\n [-4.16009689e-01 -1.74477836e+00  1.37235899e-01  1.33225943e-01]\n [ 7.95669016e-01 -5.87763531e-01  4.78430123e-01  3.96171883e-01]\n [-1.73673948e-01 -5.87763531e-01  4.21564419e-01  1.33225943e-01]\n [ 5.53333275e-01  5.69251294e-01  5.35295827e-01  5.27644853e-01]\n [-1.14301691e+00 -1.51337539e+00 -2.60824029e-01 -2.61192967e-01]\n [ 9.16836886e-01 -3.56360566e-01  4.78430123e-01  1.33225943e-01]\n [-7.79513300e-01 -8.19166497e-01  8.03701950e-02  2.64698913e-01]\n [-1.02184904e+00 -2.43898725e+00 -1.47092621e-01 -2.61192967e-01]\n [ 6.86617933e-02 -1.24957601e-01  2.50967307e-01  3.96171883e-01]\n [ 1.89829664e-01 -1.97618132e+00  1.37235899e-01 -2.61192967e-01]\n [ 3.10997534e-01 -3.56360566e-01  5.35295827e-01  2.64698913e-01]\n [-2.94841818e-01 -3.56360566e-01 -9.02269170e-02  1.33225943e-01]\n [ 1.03800476e+00  1.06445364e-01  3.64698715e-01  2.64698913e-01]\n [-2.94841818e-01 -1.24957601e-01  4.21564419e-01  3.96171883e-01]\n [-5.25060772e-02 -8.19166497e-01  1.94101603e-01 -2.61192967e-01]\n [ 4.32165405e-01 -1.97618132e+00  4.21564419e-01  3.96171883e-01]\n [-2.94841818e-01 -1.28197243e+00  8.03701950e-02 -1.29719997e-01]\n [ 6.86617933e-02  3.37848329e-01  5.92161531e-01  7.90590793e-01]\n [ 3.10997534e-01 -5.87763531e-01  1.37235899e-01  1.33225943e-01]\n [ 5.53333275e-01 -1.28197243e+00  6.49027235e-01  3.96171883e-01]\n [ 3.10997534e-01 -5.87763531e-01  5.35295827e-01  1.75297293e-03]\n [ 6.74501145e-01 -3.56360566e-01  3.07833011e-01  1.33225943e-01]\n [ 9.16836886e-01 -1.24957601e-01  3.64698715e-01  2.64698913e-01]\n [ 1.15917263e+00 -5.87763531e-01  5.92161531e-01  2.64698913e-01]\n [ 1.03800476e+00 -1.24957601e-01  7.05892939e-01  6.59117823e-01]\n [ 1.89829664e-01 -3.56360566e-01  4.21564419e-01  3.96171883e-01]\n [-1.73673948e-01 -1.05056946e+00 -1.47092621e-01 -2.61192967e-01]\n [-4.16009689e-01 -1.51337539e+00  2.35044910e-02 -1.29719997e-01]\n [-4.16009689e-01 -1.51337539e+00 -3.33612130e-02 -2.61192967e-01]\n [-5.25060772e-02 -8.19166497e-01  8.03701950e-02  1.75297293e-03]\n [ 1.89829664e-01 -8.19166497e-01  7.62758643e-01  5.27644853e-01]\n [-5.37177559e-01 -1.24957601e-01  4.21564419e-01  3.96171883e-01]\n [ 1.89829664e-01  8.00654259e-01  4.21564419e-01  5.27644853e-01]\n [ 1.03800476e+00  1.06445364e-01  5.35295827e-01  3.96171883e-01]\n [ 5.53333275e-01 -1.74477836e+00  3.64698715e-01  1.33225943e-01]\n [-2.94841818e-01 -1.24957601e-01  1.94101603e-01  1.33225943e-01]\n [-4.16009689e-01 -1.28197243e+00  1.37235899e-01  1.33225943e-01]\n [-4.16009689e-01 -1.05056946e+00  3.64698715e-01  1.75297293e-03]\n [ 3.10997534e-01 -1.24957601e-01  4.78430123e-01  2.64698913e-01]\n [-5.25060772e-02 -1.05056946e+00  1.37235899e-01  1.75297293e-03]\n [-1.02184904e+00 -1.74477836e+00 -2.60824029e-01 -2.61192967e-01]\n [-2.94841818e-01 -8.19166497e-01  2.50967307e-01  1.33225943e-01]\n [-1.73673948e-01 -1.24957601e-01  2.50967307e-01  1.75297293e-03]\n [-1.73673948e-01 -3.56360566e-01  2.50967307e-01  1.33225943e-01]\n [ 4.32165405e-01 -3.56360566e-01  3.07833011e-01  1.33225943e-01]\n [-9.00681170e-01 -1.28197243e+00 -4.31421141e-01 -1.29719997e-01]\n [-1.73673948e-01 -5.87763531e-01  1.94101603e-01  1.33225943e-01]\n [ 5.53333275e-01  5.69251294e-01  1.27454998e+00  1.71090158e+00]\n [-5.25060772e-02 -8.19166497e-01  7.62758643e-01  9.22063763e-01]\n [ 1.52267624e+00 -1.24957601e-01  1.21768427e+00  1.18500970e+00]\n [ 5.53333275e-01 -3.56360566e-01  1.04708716e+00  7.90590793e-01]\n [ 7.95669016e-01 -1.24957601e-01  1.16081857e+00  1.31648267e+00]\n [ 2.12851559e+00 -1.24957601e-01  1.61574420e+00  1.18500970e+00]\n [-1.14301691e+00 -1.28197243e+00  4.21564419e-01  6.59117823e-01]\n [ 1.76501198e+00 -3.56360566e-01  1.44514709e+00  7.90590793e-01]\n [ 1.03800476e+00 -1.28197243e+00  1.16081857e+00  7.90590793e-01]\n [ 1.64384411e+00  1.26346019e+00  1.33141568e+00  1.71090158e+00]\n [ 7.95669016e-01  3.37848329e-01  7.62758643e-01  1.05353673e+00]\n [ 6.74501145e-01 -8.19166497e-01  8.76490051e-01  9.22063763e-01]\n [ 1.15917263e+00 -1.24957601e-01  9.90221459e-01  1.18500970e+00]\n [-1.73673948e-01 -1.28197243e+00  7.05892939e-01  1.05353673e+00]\n [-5.25060772e-02 -5.87763531e-01  7.62758643e-01  1.57942861e+00]\n [ 6.74501145e-01  3.37848329e-01  8.76490051e-01  1.44795564e+00]\n [ 7.95669016e-01 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-1.24957601e-01  8.19624347e-01  1.44795564e+00]\n [ 5.53333275e-01 -1.28197243e+00  7.05892939e-01  9.22063763e-01]\n [ 7.95669016e-01 -1.24957601e-01  8.19624347e-01  1.05353673e+00]\n [ 4.32165405e-01  8.00654259e-01  9.33355755e-01  1.44795564e+00]\n [ 6.86617933e-02 -1.24957601e-01  7.62758643e-01  7.90590793e-01]]\n"
    }
   ],
   "source": [
    "# 标准化数据\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "X_norm = StandardScaler().fit_transform(X)\n",
    "print(X_norm)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   }
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# 可视化原数据与标准化数据\n",
    "from matplotlib import pyplot as plt\n",
    "fig = plt.figure(figsize=(20, 5))\n",
    "plt.subplot(121)\n",
    "plt.hist(X.loc[:, 'sepal length'], bins=100) # 只取 spepal length 这个维度\n",
    "plt.subplot(122)\n",
    "plt.hist(X_norm[:, 0], bins=100)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "5.843333333333335 0.8280661279778629 -4.736951571734001e-16 1.0\n"
    }
   ],
   "source": [
    "# 计算原数据与标准化数据的均值、方差\n",
    "X1_mean = X.loc[:, 'sepal length'].mean()\n",
    "X1_sigma = X.loc[:, 'sepal length'].std()\n",
    "X1_norm_mean = X_norm[:, 0].mean()\n",
    "X1_norm_sigma = X_norm[:, 0].std()\n",
    "print(X1_mean, X1_sigma, X1_norm_mean, X1_norm_sigma)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   }
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "[0.72770452 0.23030523 0.03683832 0.00515193]\n"
    }
   ],
   "source": [
    "# 降维处理\n",
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=4) # 原数据维度是4，首先进行同等维度的PCA处理\n",
    "X_pac = pca.fit_transform(X_norm) # 这里是PCA处理后的主成分\n",
    "# 计算各维度下主成分方差比例\n",
    "var_ratio = pca.explained_variance_ratio_\n",
    "print(var_ratio)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   }
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# 可视化各方差\n",
    "fig1 = plt.figure(figsize=(20, 5))\n",
    "plt.bar([1, 2, 3, 4], var_ratio)\n",
    "plt.xticks([1, 2, 3, 4], ['PC1', 'PC2', 'PC3', 'PC4']) # x轴更名\n",
    "plt.ylabel('variance ratio of each PC')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(150, 2)"
     },
     "metadata": {},
     "execution_count": 50
    }
   ],
   "source": [
    "# 继续降维处理\n",
    "# 方差越大相关性越小，方差越小相关性越大，所以只取PC1，PC2，降为2维\n",
    "pca = PCA(n_components=2)\n",
    "X_pac = pca.fit_transform(X_norm)\n",
    "X_pac.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# 可视化\n",
    "fig2 = plt.figure(figsize=(10, 5))\n",
    "setosa = plt.scatter(X_pac[:, 0][y==0], X_pac[:, 1][y==0])\n",
    "versicolor = plt.scatter(X_pac[:, 0][y==1], X_pac[:, 1][y==1])\n",
    "virginica = plt.scatter(X_pac[:, 0][y==2], X_pac[:, 1][y==2])\n",
    "plt.legend((setosa, versicolor, virginica), ('setosa', 'versicolor', 'virginica'))\n",
    "plt.xlabel('PC1')\n",
    "plt.ylabel('PC2')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "0.96\n0.9466666666666667\n"
    }
   ],
   "source": [
    "# 降维后的模型准确率\n",
    "KNN = KNeighborsClassifier(n_neighbors=3)\n",
    "KNN.fit(X_pac, y)\n",
    "y_predict = KNN.predict(X_pac)\n",
    "accuracy_pca = accuracy_score(y, y_predict)\n",
    "print(accuracy)\n",
    "print(accuracy_pca)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   }
  }
 ]
}